Principal Engineer, AI
Measured
full-time
Posted on:
Location Type: Remote
Location: Texas • United States
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Job Level
About the role
- Drive the technical roadmap for AI/ML systems, aligning model development and deployment initiatives with key business outcomes.
- Establish and champion scalable AI delivery practices (evaluation, deployment, monitoring, governance) and production-grade architecture patterns and engineering standards.
- Identify and address strategic technical debt and performance bottlenecks within the core AI/ML platform and inference services.
- Lead the adoption of emerging AI research, foundational models, and deep learning frameworks that future-proof our platform's intelligence capabilities.
- Build agentic workflows that can query metrics, run analyses, and cite supporting data
- Implement RAG patterns over internal schemas/data
- Establish an evaluation and guardrails framework: monitoring for drift/hallucinations, along with PII handling, tenant isolation, policy controls, and audit logging.
- Oversee production pipelines for AI services and ML workflows, including deployment, monitoring, and governance.
- Lead the development and evolution of scalable tools for prompt/agent versioning, experimentation, evaluation, release management, and monitoring.
- Ensure robust data validation, model testing practices, continuous integration/continuous delivery (CI/CD) for ML models, and automated deployment of AI services are in place.
- Promote engineering best practices for building explainable, ethical, and bias-aware AI systems through agile methodologies and continuous improvement.
- Build, mentor, and grow high-performing AI Engineering, Data Science, and AI Research teams and technical leaders.
- Partner with executive leadership to define and execute technical strategies for integrating AI across product lines and departments.
- Foster a culture of rigorous scientific experimentation, accountability in model performance, and cross-functional collaboration between research, data, and engineering.
- Serve as a senior technical advisor on complex AI/ML architectural decisions, providing guidance on model selection and influencing data product direction.
- Whatever else it takes to get the job done!
Requirements
- 10+ years of software engineering experience with a focus on modern technologies.
- 5+ years operating at a senior or principal engineering level, with demonstrated impact scaling teams, systems, and platforms.
- Strong experience building and operating production AI/ML systems, including model deployment and monitoring, and optimizing scalable, high-performance AI infrastructure.
- Hands-on experience building LLM-powered applications (e.g., OpenAI/Anthropic), including prompt/tool design, function calling, and RAG with vector databases
- Strong evaluation and production engineering background: LLM eval frameworks/experimentation, backend & distributed systems fundamentals, and strong SQL/warehouse fluency (Snowflake/Redshift/BigQuery)
- Strong background in component-based architecture, design systems, and performance.
- Technical proficiency in CI/CD, automated testing, Git workflows, and build systems.
- Familiarity with cloud platforms (AWS, GCP, or Azure), RESTful APIs, and GraphQL.
- Exceptional communication, decision-making, and problem-solving skills.
- A demonstrated ability to drive organizational change and technical innovation at scale.
Benefits
- 100% Remote
- Total Rewards - Compelling compensation packages that include flexible time off, regional paid holidays, and regional health and wellness plans where available
- Social Engagement - virtual engagement, knowledge sharing, and more
- Giving Back - Opportunities to volunteer and impact our communities through Measured for Good initiatives
- Culture - Integrity, diversity, and award winning technology
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
AI systemsML systemsmodel deploymentmonitoringproduction engineeringLLM-powered applicationsSQLCI/CDautomated testingcomponent-based architecture
Soft Skills
communicationdecision-makingproblem-solvingleadershipcollaborationmentoringorganizational changetechnical innovationaccountabilityagile methodologies